Review and evaluation of reinforcement learning frameworks on smart grid applications
With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …
emissions, there is an increasing demand for effective energy management in energy …
Intent-driven intelligent control and orchestration in o-ran via hierarchical reinforcement learning
MA Habib, H Zhou, PE Iturria-Rivera… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
rApps and xApps need to be controlled and orchestrated well in the open radio access
network (O-RAN) so that they can deliver a guaranteed network performance in a complex …
network (O-RAN) so that they can deliver a guaranteed network performance in a complex …
Energy efficient edge computing enabled by satisfaction games and approximate computing
In this paper, we introduce an energy efficient edge computing solution to collaboratively
utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to …
utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to …
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to
provide wireless connectivity to ground devices in events of increased network demand …
provide wireless connectivity to ground devices in events of increased network demand …
Energy efficient edge computing: When lyapunov meets distributed reinforcement learning
In this work, we study the problem of energy-efficient computation offloading enabled by
edge computing. In the considered scenario, multiple users simultaneously compete for …
edge computing. In the considered scenario, multiple users simultaneously compete for …
Reinforcing the edge: Autonomous energy management for mobile device clouds
V Balasubramanian, F Zaman… - … -IEEE conference on …, 2019 - ieeexplore.ieee.org
The collaboration among mobile devices to form an edge cloud for sharing computation and
data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover …
data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover …
[PDF][PDF] Model-free real-time autonomous energy management for a residential multi-carrier energy system: A deep reinforcement learning approach
The problem of real-time autonomous energy management is an application area that is
receiving unprecedented attention from consumers, governments, academia and industry …
receiving unprecedented attention from consumers, governments, academia and industry …
Optimizing energy efficiency in UAV-assisted networks using deep reinforcement learning
In this letter, we study the energy efficiency (EE) optimization of unmanned aerial vehicles
(UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent …
(UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent …
Optimal computation resource allocation in energy-efficient edge IoT systems with deep reinforcement learning
This paper investigates a computation resource optimization problem of mobile edge
computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) …
computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) …
Reinforcement learning for intelligent healthcare systems: A review of challenges, applications, and open research issues
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …
expenditure and mortality rates. This calls for transforming healthcare systems away from …